Introduces the latest developments in forecasting in advanced quantitative data analysis
This book presents advanced univariate multiple regressions, which can directly be used to forecast their dependent variables, evaluate their in-sample forecast values, and compute forecast values beyond the sample period. Various alternative multiple regressions models are presented based on a single time series, bivariate, and triple time-series, which are developed by taking into account specific growth patterns of each dependent variables, starting with the simplest model up to the most advanced model. Graphs of the observed scores and the forecast evaluation of each of the models are offered to show the worst and the best forecast models among each set of the models of a specific independent variable.
Advanced Time Series Data Analysis: Forecasting Using EViews provides readers with a number of modern, advanced forecast models not featured in any other book. They include various interaction models, models with alternative trends (including the models with heterogeneous trends), and complete heterogeneous models for monthly time series, quarterly time series, and annually time series. Each of the models can be applied by all quantitative researchers.
• Presents models that are all classroom tested
• Contains real-life data samples
• Contains over 350 equation specifications of various time series models
• Contains over 200 illustrative examples with special notes and comments
• Applicable for time series data of all quantitative studies
Advanced Time Series Data Analysis: Forecasting Using EViews will appeal to researchers and practitioners in forecasting models, as well as those studying quantitative data analysis. It is suitable for those wishing to obtain a better knowledge and understanding on forecasting, specifically the uncertainty of forecast values.
Author(s): I. Gusti Ngurah Agung
Edition: 1
Publisher: Wiley
Year: 2019
Language: English
Commentary: Vector PDF
Pages: 544
City: Hoboken, NJ
Tags: Statistics; Forecasting; Time Series Analysis; Eviews
Title Page
Copyright Page
Contents
About the Author
Preface
Chapter 1 Forecasting a Monthly Time Series
1.1 Introduction
1.2 Forecasting Using LV(p) Models
1.2.1 Basic or Regular LV(p) Models
1.2.2 Special LV(p) Models
1.3 Forecasting Using the LVARMA(p,q,r) Model
1.3.1 Special Notes on the ARMA Model
1.3.2 Application of Special LVAR Models
1.4 Forecasting Using TGARCH(a,b,c) Models
1.4.1 Application of ARCH(a), GARCH(b), and TARCH(c) Models
1.4.2 Application of TGARCH(a,b,0) Models
1.4.3 Application of TGARCH(a,b,c) Models
1.4.4 Other Alternative Models
1.5 Instrumental Variables Models
1.5.1 Application of the GMM Estimation Method
1.5.2 Application of the TSLS Estimation Method
1.6 Special Notes and Comments on Residual Analysis
1.6.1 Specific Residual Analysis
1.6.2 Additional Special Notes and Comments
1.6.3 Serial Correlation Tests
1.7 Statistical Results Using Alternative Options
1.7.1 Application of an Alternative Coefficient Covariance Matrix
1.7.2 Application of Selected Combinations of Options
1.7.3 Final Notes and Conclusions
Chapter 2 Forecasting with Time Predictors
2.1 Introduction
2.2 Application of LV(p) Models of HS on MONTH by YEAR
2.2.1 Special LV(12) Models of HS on MONTH by YEAR
2.2.2 Application of the Omitted Variables Test – Likelihood Ratio
2.2.3 Heterogeneous Model of HS on HS(-12) and Month by YEAR
2.3 Forecast Models of HS on MONTH by YEAR
2.3.1 Application of LV(1) Models of HS on MONTH by YEAR
2.3.2 Application of Basic LV(p) Models of HS on MONTH by YEAR
2.3.3 Application of AR(q) Models of HS on MONTH by YEAR
2.3.4 Application of ARMA(q,r) Models of HS on MONTH by YEAR
2.3.5 Application of LVAR(p,q) Models of HS on MONTH by YEAR
2.3.6 Application of LVAR(p,q) Models of HS on YEAR by MONTH
2.4 Heterogeneous Classical Growth Models
2.4.1 Forecasting Based on LV(p) Het_CGMs of HS
2.4.2 Forecasting Based on AR(q) Het_CGMs
2.4.3 Forecasting Based on LVAR(p,q) Het_CGMs
2.5 Forecast Models of G in Currency.wf1
2.5.1 LVAR(p,q) Additive Models of G by @Month with @Trend
2.5.2 LV(1) Heterogeneous Models of G by @Month
2.6 Forecast Models of G on G(-1) and Polynomial Time Variables
2.6.1 Heterogeneous Model of G on G(-1) and Polynomial T by @Month
2.6.2 Forecast Model of G on G(-1) with Heterogeneous Polynomial Trend
2.7 Forecast Models of CURR in Currency.wf1
2.7.1 Developing Scatter Graphs with Regressions
2.7.2 Additive Forecast Models of CURR with a Time Predictor
2.7.3 Interaction Forecast Models of CURR
2.7.4 Forecast Models Based on Subsamples
Chapter 3 Continuous Forecast Models
3.1 Introduction
3.2 Forecasting of FSPCOM
3.2.1 Simple Continuous Models of FSPCOM
3.2.2 LVAR(p,q) Models of Y = FSPCOM with Polynomial Trend
3.2.3 Translog Models with Time Predictor
3.3 Forecasting Based on Subsamples
3.3.1 Lag Variable Models With Lower and Upper Bounds
3.4 Special LV(12) Models of HS with Upper and Lower Bounds
3.4.1 Special LVARMA(12,q,r) Model of LNYul Without Time Predictor
3.4.2 Special LVARMA(12,q,r) of LNYul With Time Predictor
Chapter 4 Forecasting Based on (Xt,Yt)
4.1 Introduction
4.2 Forecast Models Based on (Xt,Yt)
4.3 Data Analysis Based on a Monthly Time Series
4.4 Forecast Models without a Time Predictor
4.4.1 Two-Way Interaction Models
4.4.2 Cobb–Douglass Model and Alternatives
4.5 Translog Quadratic Model
4.5.1 Forecasting Using a Subsample
4.5.2 Forecast Model with Trend
4.6 Forecasting of FSXDP
4.6.1 Forecasting of Y2 Based on a Subsample
4.6.2 Extension of the Model with Time Variables
4.7 Translog Linear Models
4.7.1 Basic Translog Linear Model
4.7.2 Tanslog Linear Model with Trend
4.7.3 Heterogeneous Tanslog Linear Model
4.8 Application of VAR Models
4.8.1 Unstructured VAR Models Based on (X1t,Y1t)
4.8.2 The Simplest VAR Models with Alternative Trends
4.8.3 Complete Heterogeneous VAR Models by @Month
4.8.4 Bayesian VAR Models
4.8.5 VEC Models
4.9 Forecast Models Based on (Y1t,Y2t)
4.9.1 Forecast Models Based on Figuresa and b
4.9.2 Reciprocal Causal Effects Models
4.9.3 Models with the Time Independent Variables
4.10 Special Notes and Comments
Chapter 5 Forecasting Based on (X1t,X2t,Yt)
5.1 Introduction
5.2 Translog Linear Models Based on (X1,X2,Y1)
5.2.1 Basic Translog Linear Model
5.2.2 Tanslog Linear Model with Trend
5.2.3 Tanslog Linear Model with Heterogeneous Trends
5.3 Translog Linear Models Based on (X1,X2,Y2)
5.3.1 Translog Linear Models Using the Subsample {@Year>1990}
5.3.2 Translog Linear Models Using the Subsample {@Year>1975}
5.3.3 Translog Linear Models Using the Whole Sample
5.4 Forecast Models Using Original (X1,X2,Y)
5.4.1 Model Based on Figurea
5.4.2 Model Based on Figureb
5.4.3 Model Based on Figurec
5.5 Alternative Forecast Models Using Original (X1,X2,Y)
5.5.1 Three-Way Interaction Based on Figurea
5.5.2 Three-Way Interaction Based on Figureb and c
5.6 Forecasting Models with Trends Using Original (X1,X2,Y)
5.7 Application of VAR Models Based on (X1t,X2t,Y1t)
5.7.1 Unrestricted VAR Models
5.7.2 The Simplest Two-Way Interaction VAR Model
5.7.3 The Simplest Three-Way Interaction VAR Model
5.8 Applications of the Object ``System´´
5.8.1 The MLV(1,1,1) Models of (Y1,Y2,Y3) on (Y1(-1),Y2(-1),Y3(-1))
5.8.2 Circular Effects MLV(1,1,1) Models
5.9 Models Presenting Causal Relationships Y1,Y2, and Y3
5.9.1 Triangular Effects Models
5.9.2 Circular Effects Models
5.9.3 Reciprocal Effects Models
5.10 Extended Models
5.10.1 Extension to the Models with Additional Exogenous Variables
5.10.2 Extension to the Models with Alternative Trends
5.10.3 Extension to LVARMA(p,q,r)
5.10.4 Extension to Heterogeneous Regressions by Months
5.11 Special Notes and Comments
Chapter 6 Forecasting Quarterly Time Series
6.1 Introduction
6.2 Alternative LVARMA(p,q,r) of a Single Time Series
6.2.1 LV(p) Forecast Model of GCDANt
6.2.2 LVARMA(p,q.r) Forecast Models of GCDN
6.2.3 Forecast Models of GCDAN with Time Variables
6.2.4 Special Notes on Uncommon Models
6.3 Complete Heterogeneous LV(2) Models of GCDAN By @Quarter
6.3.1 Using the Simplest Equation Specification
6.3.2 Using a Complete Equation Specification
6.4 LV(2) Models of GCDAN with Exogenous Variables
6.4.1 LV(2) Models with an Exogenous Variable
6.4.2 LV(2) Models with Two Exogenous Variables
6.5 Alternative Forecast Models Based on (Y1,Y2)
6.5.1 LV(2) Basic Interaction Models
6.5.2 LV(2) Models of (Y1,Y2) with an Exogenous Variable and @Trend
6.5.3 LV(2) Models of (Y1,Y2) with two Exogenous Variables and Trend
6.5.4 LV(2) Models of (Y1,Y2) with Three Exogenous Variables and Trend
6.6 Triangular Effects Models Based on (X1,X2,Y1)
6.6.1 Partial Two-Way Interaction LV(p) TE_Models
6.6.2 A Complete Two-Way Interaction LV(p) TE_Models
6.6.3 Three-Way Interaction LV(p) TE_ Models
6.7 Bivariate Triangular Effects Models Based on (X1,X2,Y1,Y2)
6.7.1 Partial Two-Way Interaction Models
6.7.2 Three-Way Interaction TE_Models
6.8 Models with Exogenous Variables and Alternative Trends
6.8.1 Models Based on (X1,X2,Y1)
6.8.2 Models Based on (X1,X2,Y1,Y2) with Trend
6.9 Special LV(4) Models with Exogenous Variables
6.10 Models with Exogenous Variables by @Quarter
6.10.1 Alternative Models Based on the Whole Sample
6.10.2 Forecasting Based on each Quarter's Level
Chapter 7 Forecasting Based on Time Series by States
7.1 Introduction
7.2 Models Based on a Bivariate (Y1_1,Y1_2)
7.2.1 Alternative LV(p) Models Based on Figurea
7.2.2 Alternative LV(p) Models Based on Figureb
7.2.3 Alternative LV(p) Models Based on Figurec
7.3 Advanced LP(p) Models of (Y1_1,Y1_2)
7.3.1 Two-Way Interaction LV(p) Models
7.3.2 Three-Way Interaction LV(p) Models
7.3.3 Alternative Additive Models
7.4 Advanced LP(p) Models of (Y1_1,Y1_2,Y1_3)
7.4.1 Triangular Effects Model of (Y1_1,Y1_2,Y1_3)
7.4.2 Full-Lag Variables Triangular Effects Model
7.4.3 Translog-Linear Triangular Effects Model
7.5 Full-Lag Variables Circular Effects Model
7.5.1 Two-Way Interaction Circular Effects Models
7.5.2 Three-Way Interaction Circular Effects Models
7.6 Full-Lag Variables Reciprocal-Effects Model
7.6.1 Two-Way Interaction Reciprocal-Effects Models
7.6.2 Three-Way Interaction Reciprocal-Effects Models
7.7 Successive Up-and-Downstream Relationships
7.7.1 A Set of the Simplest Two-Way Interaction Models
7.7.2 Successive Two-Way Interaction Triangular Effects Models
7.7.3 Successive Three-Way Interaction Triangular Effects Models
7.8 Forecast Models with the Time Independent Variable
7.8.1 Forecast Models with Alternative Trends
7.8.2 Two-Way Interaction with Time-Related Effects Models
7.8.3 Three-Way Interaction Time-Related Effects Models
7.9 Final Notes and Comments
7.9.1 The Manual Multistage Selection Method
7.9.2 Notes on the Best Possible Forecast Models
Bibliography
Index
EULA
